In Part 2, we looked at the question of productivity from a regional perspective, asking how universities can better contribute to economic growth in their area. The solution we proposed showed how universities could use Labour Market Intelligence (LMI) to identify regional employer demand for graduates, which could then be used to feed back into creating a course portfolio that better reflects those demands.
However, much as this might help universities that want to have a greater effect on regional productivity, what about universities that have a lower proportion of students staying in their region after graduation, or those universities that do not necessarily want to focus attention on their region, but prefer to have a wider influence on productivity throughout the country?
There are essentially three approaches that a university might take. The first is really just business as usual; that is, continuing on the same normative model of producing a supply of graduates, without identifying the demand. Does this model really help increase productivity? Not really, since under this model there is no way of knowing whether the supply of graduates going into the workplace is really what is needed. In fact, we can go further and assert that since there are clear skills gaps at graduate level, this model is not effective.
A better approach would be to look at demand for certain occupations across the national economy and to tailor course provision accordingly. So for instance, a university with an engineering department might identify the nationwide demand for related occupations throughout the country and tailor their course portfolio accordingly. However, whilst this approach is more likely to meet employer demand than the first, it is only marginally so. There may well be a demand for chemical engineers, but where is this demand? Without knowing this, the model still resembles something of a scattergun approach, far from the precision approach that is needed if we are to see real impacts on productivity.
We want to suggest a third model; one which we believe is far more precise and far more likely to have a real impact on productivity since it uses data to link universities with skills specialisms to industries across the country that are in need of those skills.
To take a hypothetical example, think of a university which offers degrees in Forestry. They may have links with local companies involved in the forestry and logging sector, but how about the rest of the country? Using LMI for the whole of the country, we can begin by quickly identifying where the major hotspots in the country are for this industry (NB. This sector is made up of four sub-industries, which are Silviculture and other forestry activities; Logging; Gathering of wild growing non-wood products; Support services to forestry):
The data can also identify which areas of the country are likely to see the most growth over the next few years. In this instance it is the South East, which is set to see around 383 new jobs created in the sector between 2015 and 2020:
By digging deeper into the data — down to the local authority level — we can even begin to get a more detailed view of which sub-geographies within the South East are set to see growth:
This approach can be highly useful in terms of identifying industry needs and growth which are connected with the university’s course portfolio. But what makes this exercise even more useful is going on to identify the local employers in those growth areas. For instance, in the case of the Forestry industry in the South East, the biggest three employers are Tilhill Forestry Limited, The Tropical Forest Trust, and Euroforest Limited (this information is taken from Equifax data, which we have linked to all our industry data in our Analyst tool, allowing users to identify employers for any industry, along with contact details).
By taking this approach, any university can quickly and simply identify demand in those industries that are connected with course provision, for any part of the country. In addition, they can identify the employers in those industries, which can give rise to tremendous opportunities for linking with those companies to become an ongoing supplier of talent. This approach has obvious ramifications for increased productivity, as well as for increasing employability.
But there is more. Using granular data gives universities the opportunity to consolidate and strengthen their existing course portfolio in ways that can lead to increased productivity, and it also affords opportunities to diversify to include new degrees, or perhaps Higher or Degree Apprenticeships, either in areas that they have not yet explored at all, or in areas related to existing courses.
For instance, sticking with the Forestry industry, the data can be used to identify the occupations that this sector employs. The following table shows the Top 10 graduate positions within the industry in Britain:
|Description||Employed in Industry Group (2015)||Employed in Industry Group (2020)||Median Hourly Earnings|
|Human resource managers and directors||221||221||£23.83|
|Biological scientists and biochemists||194||206||£18.87|
|Management consultants and business analysts||94||83||£21.00|
|Financial managers and directors||93||100||£29.19|
|Functional managers and directors n.e.c.||76||82||£24.47|
|Chartered and certified accountants||47||49||£19.84|
|IT project and programme managers||35||39||£24.19|
|Business and financial project management professionals||33||33||£22.49|
This shows some of the other occupational requirements of the Forestry industry, outside the starting point of a degree in Forestry. So if a university has taken the approach described above — identifying industries where there is likely to be growth and forging strong links with the employers in the sector — they can take this a step further by identifying other graduate needs within the same industry. This can then be used to inform their course portfolio to include degrees and/or other forms of provision such as Higher/Degree Apprenticeships that relate to other in-demand skills in the sector.
The current supply-led approach adopted by the majority of universities is unlikely to impact positively on productivity. A generalised demand-led approach will not get us much further. However, the approach we have set out above, where a university uses highly specific data to identify industries, employers and occupations that are related to their current course portfolio, or to identify possible areas of diversification, could well have a dramatic impact on productivity, not to mention the other side of the coin, employability. In the next part of this series we’ll look at employability in more detail.
You can access parts 1 and 2 here and here. For more information on how we can help your university improve productivity and employability, contact Jamie Mackay at firstname.lastname@example.org.